We explore a new Bayesian model for probabilistic grammars, a family of distributions over discrete structures that includes hidden Markov models and probabilistic context-free gr...
We present a new reordering model estimated as a standard n-gram language model with units built from morphosyntactic information of the source and target languages. It can be see...
In this paper we evaluate a method for generating synthetic speech at high speaking rates based on the interpolation of hidden semi-Markov models (HSMMs) trained on speech data re...
Michael Pucher, Dietmar Schabus, Junichi Yamagishi
In the traditional mixture of Gaussians background model, the generating process of each pixel is modeled as a mixture of Gaussians over color. Unfortunately, this model performs ...
This paper proposes a method for automatic POS (part-of-speech) guessing of Chinese unknown words. It contains two models. The first model uses a machinelearning method to predict...